scholarly journals Arctic sea ice response to flooding of the snow layer in future warming scenarios

2021 ◽  
Author(s):  
Andrew G. Pauling ◽  
Cecilia M. Bitz
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Snow Layer ◽  
Arctic Sea ◽  
Future Warming

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

scholarly journals Ocean Heat Transport as a Cause for Model Uncertainty in Projected Arctic Warming

2011 ◽  
Vol 24 (5) ◽  
pp. 1451-1460 ◽  
Author(s):  
Irina Mahlstein ◽  
Reto Knutti
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
General Circulation ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ocean Heat Transport ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Future Warming

Abstract The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution. These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.

scholarly journals Brief Communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

2019 ◽  
Author(s):  
Robbie D. C. Mallett ◽  
Isobel R. Lawrence ◽  
Julienne C. Stroeve ◽  
Jack C. Landy ◽  
Michel Tsamados
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Snow Layer ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Satellite Radar ◽  
Arctic Sea Ice Thickness ◽  
Radar Wave

Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emit Ku-band radar waves that are conventionally assumed to penetrate overlying snow and scatter from the ice-snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time-evolution of overlying snow density. Two conventional treatments lead to systematic underestimates of winter ice thickness and thermodynamic growth rate of up to 15 cm over multiyear ice. Correcting these biases would improve the accuracy of sea ice thickness products, which feed a wide variety of model projections, calibrations, validations and reanalyses.

scholarly journals Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

2020 ◽  
Vol 14 (1) ◽  
pp. 251-260 ◽  
Author(s):  
Robbie D. C. Mallett ◽  
Isobel R. Lawrence ◽  
Julienne C. Stroeve ◽  
Jack C. Landy ◽  
Michel Tsamados
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Snow Layer ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Satellite Radar ◽  
Arctic Sea Ice Thickness ◽  
Radar Wave

Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses.

scholarly journals The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover

2013 ◽  
Vol 7 (4) ◽  
pp. 1193-1204 ◽  
Author(s):  
A. A. Marks ◽  
M. D. King
Keyword(s):  
Sea Ice ◽  
Black Carbon ◽  
Cross Sections ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
First Year ◽  
Transfer Model ◽  
Snow Layer ◽  
Arctic Sea ◽  
Visible Wavelengths

Abstract. The response of the albedo of bare sea ice and snow-covered sea ice to the addition of black carbon is calculated. Visible light absorption and light-scattering cross-sections are derived for a typical first-year and multi-year sea ice with both "dry" and "wet" snow types. The cross-sections are derived using data from a 1970s field study that recorded both reflectivity and light penetration in Arctic sea ice and snow overlying sea ice. The variation of absorption cross-section over the visible wavelengths suggests black carbon is the dominating light-absorbing impurity. The response of first-year and multi-year sea ice albedo to increasing black carbon, from 1 to 1024 ng g−1, in a top 5 cm layer of a 155 cm-thick sea ice was calculated using a radiative-transfer model. The albedo of the first-year sea ice is more sensitive to additional loadings of black carbon than the multi-year sea ice. An addition of 8 ng g−1 of black carbon causes a decrease to 98.7% of the original albedo for first-year sea ice compared to a decrease to 99.7% for the albedo of multi-year sea ice, at a wavelength of 500 nm. The albedo of sea ice is surprisingly unresponsive to additional black carbon up to 100 ng g−1 . Snow layers on sea ice may mitigate the effects of black carbon in sea ice. Wet and dry snow layers of 0.5, 1, 2, 5 and 10 cm depth were added onto the sea ice surface. The albedo of the snow surface was calculated whilst the black carbon in the underlying sea ice was increased. A layer of snow 0.5 cm thick greatly diminishes the effect of black carbon in sea ice on the surface albedo. The albedo of a 2–5 cm snow layer (less than the e-folding depth of snow) is still influenced by the underlying sea ice, but the effect of additional black carbon in the sea ice is masked.

scholarly journals Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

2013 ◽  
Vol 7 (6) ◽  
pp. 1971-1989 ◽  
Author(s):  
N. Maaß ◽  
L. Kaleschke ◽  
X. Tian-Kunze ◽  
M. Drusch
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Snow Layer ◽  
Brightness Temperatures ◽  
Arctic Sea ◽  
L Band ◽  
Snow Thickness

Abstract. The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r2 = 0.58.

scholarly journals Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

2013 ◽  
Vol 7 (4) ◽  
pp. 3627-3674 ◽  
Author(s):  
N. Maaß ◽  
L. Kaleschke ◽  
X. Tian-Kunze ◽  
M. Drusch
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Snow Layer ◽  
Brightness Temperatures ◽  
Arctic Sea ◽  
L Band ◽  
Snow Thickness

Abstract. The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.0 K to 4.4 K, when we include the snow layer in the simulations. Under cold Arctic conditions we find brightness temperatures to increase with increasing snow thickness. Because dry snow is almost transparent in L-band, this brightness temperature's dependence on snow thickness origins from the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.7 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 6.3 cm, and the correlation coefficient is r2 = 0.55.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

Eastern-Western Arctic Sea Ice Analysis: 1988

1988 ◽  
Author(s):  
NAVAL POLAR OCEANOGRAPHY CENTER WASHINGTON DC
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Western Arctic
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